import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_SCREAMING_SNAKE_CASE     				=		get_tests_dir('fixtures/test_sentencepiece.model')



@require_sentencepiece
@require_tokenizers
class 							a				(     __lowerCAmelCase			,     unittest.TestCase			):







 """simple docstring"""






 lowerCamelCase      :Optional[Any]            =						XLMRobertaTokenizer
 lowerCamelCase      :Dict            =						XLMRobertaTokenizerFast
 lowerCamelCase      :Any            =						True
 lowerCamelCase      :List[str]            =						True



 def 	UpperCAmelCase				(   self				)    ->   Optional[Any]:
        super().setUp()

        # We have a SentencePiece fixture for testing
        _A	  =    XLMRobertaTokenizer(lowercase_						,							keep_accents=lowercase_				)
        tokenizer.save_pretrained(self.tmpdirname				)



 def 	UpperCAmelCase				(   self				)    ->   Dict:
        _A	  =    """<pad>"""
        _A	  =    1

        self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase_				)						,							lowercase_				)
        self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase_				)						,							lowercase_				)



 def 	UpperCAmelCase				(   self				)    ->   Tuple:
        _A	  =    list(self.get_tokenizer().get_vocab().keys()				)

        self.assertEqual(vocab_keys[0]						,							"""<s>"""				)
        self.assertEqual(vocab_keys[1]						,							"""<pad>"""				)
        self.assertEqual(vocab_keys[-1]						,							"""<mask>"""				)
        self.assertEqual(len(lowercase_				)						,							10_02				)



 def 	UpperCAmelCase				(   self				)    ->   Optional[Any]:
        self.assertEqual(self.get_tokenizer().vocab_size						,							10_02				)



 def 	UpperCAmelCase				(   self				)    ->   int:
        _A	  =    XLMRobertaTokenizer(lowercase_						,							keep_accents=lowercase_				)

        _A	  =    tokenizer.tokenize("""This is a test"""				)
        self.assertListEqual(lowercase_						,							["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]				)

        self.assertListEqual(
            tokenizer.convert_tokens_to_ids(lowercase_				)						,							[value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]						,							)

        _A	  =    tokenizer.tokenize("""I was born in 92000, and this is falsé."""				)
        self.assertListEqual(
            lowercase_						,							[
                SPIECE_UNDERLINE + """I""",
                SPIECE_UNDERLINE + """was""",
                SPIECE_UNDERLINE + """b""",
                """or""",
                """n""",
                SPIECE_UNDERLINE + """in""",
                SPIECE_UNDERLINE + """""",
                """9""",
                """2""",
                """0""",
                """0""",
                """0""",
                """,""",
                SPIECE_UNDERLINE + """and""",
                SPIECE_UNDERLINE + """this""",
                SPIECE_UNDERLINE + """is""",
                SPIECE_UNDERLINE + """f""",
                """al""",
                """s""",
                """é""",
                """.""",
            ]						,							)
        _A	  =    tokenizer.convert_tokens_to_ids(lowercase_				)
        self.assertListEqual(
            lowercase_						,							[
                value + tokenizer.fairseq_offset
                for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
                #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
            ]						,							)

        _A	  =    tokenizer.convert_ids_to_tokens(lowercase_				)
        self.assertListEqual(
            lowercase_						,							[
                SPIECE_UNDERLINE + """I""",
                SPIECE_UNDERLINE + """was""",
                SPIECE_UNDERLINE + """b""",
                """or""",
                """n""",
                SPIECE_UNDERLINE + """in""",
                SPIECE_UNDERLINE + """""",
                """<unk>""",
                """2""",
                """0""",
                """0""",
                """0""",
                """,""",
                SPIECE_UNDERLINE + """and""",
                SPIECE_UNDERLINE + """this""",
                SPIECE_UNDERLINE + """is""",
                SPIECE_UNDERLINE + """f""",
                """al""",
                """s""",
                """<unk>""",
                """.""",
            ]						,							)



 def 	UpperCAmelCase				(   self				)    ->   Dict:







        if not self.test_slow_tokenizer:
               # as we don't have a slow version, we can't compare the outputs between slow and fast versions
               return

        _A	  =    (self.rust_tokenizer_class, """hf-internal-testing/tiny-xlm-roberta""", {})
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''				):
                      _A	  =    self.rust_tokenizer_class.from_pretrained(lowercase_						,							**lowercase_				)
                      _A	  =    self.tokenizer_class.from_pretrained(lowercase_						,							**lowercase_				)

                      _A	  =    tempfile.mkdtemp()

                      _A	  =    tokenizer_r.save_pretrained(lowercase_				)
                      _A	  =    tokenizer_p.save_pretrained(lowercase_				)

                      # Checks it save with the same files + the tokenizer.json file for the fast one
                      self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files				)				)
                      _A	  =    tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f				)
                      self.assertSequenceEqual(lowercase_						,							lowercase_				)

                      # Checks everything loads correctly in the same way
                      _A	  =    tokenizer_r.from_pretrained(lowercase_				)
                      _A	  =    tokenizer_p.from_pretrained(lowercase_				)

                      # Check special tokens are set accordingly on Rust and Python
                      for key in tokenizer_pp.special_tokens_map:
                             self.assertTrue(hasattr(lowercase_						,							lowercase_				)				)
                             # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
                             # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

                      shutil.rmtree(lowercase_				)

                      # Save tokenizer rust, legacy_format=True
                      _A	  =    tempfile.mkdtemp()

                      _A	  =    tokenizer_r.save_pretrained(lowercase_						,							legacy_format=lowercase_				)
                      _A	  =    tokenizer_p.save_pretrained(lowercase_				)

                      # Checks it save with the same files
                      self.assertSequenceEqual(lowercase_						,							lowercase_				)

                      # Checks everything loads correctly in the same way
                      _A	  =    tokenizer_r.from_pretrained(lowercase_				)
                      _A	  =    tokenizer_p.from_pretrained(lowercase_				)

                      # Check special tokens are set accordingly on Rust and Python
                      for key in tokenizer_pp.special_tokens_map:
                             self.assertTrue(hasattr(lowercase_						,							lowercase_				)				)

                      shutil.rmtree(lowercase_				)

                      # Save tokenizer rust, legacy_format=False
                      _A	  =    tempfile.mkdtemp()

                      _A	  =    tokenizer_r.save_pretrained(lowercase_						,							legacy_format=lowercase_				)
                      _A	  =    tokenizer_p.save_pretrained(lowercase_				)

                      # Checks it saved the tokenizer.json file
                      self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files				)				)

                      # Checks everything loads correctly in the same way
                      _A	  =    tokenizer_r.from_pretrained(lowercase_				)
                      _A	  =    tokenizer_p.from_pretrained(lowercase_				)

                      # Check special tokens are set accordingly on Rust and Python
                      for key in tokenizer_pp.special_tokens_map:
                             self.assertTrue(hasattr(lowercase_						,							lowercase_				)				)

                      shutil.rmtree(lowercase_				)



 @cached_property
 def 	UpperCAmelCase				(   self				)    ->   Optional[int]:
        return XLMRobertaTokenizer.from_pretrained("""xlm-roberta-base"""				)



 def 	UpperCAmelCase				(   self				)    ->   int:







        with tempfile.NamedTemporaryFile() as f:
               shutil.copyfile(lowercase_						,							f.name				)
               _A	  =    XLMRobertaTokenizer(f.name						,							keep_accents=lowercase_				)
               _A	  =    pickle.dumps(lowercase_				)
        pickle.loads(lowercase_				)



 def 	UpperCAmelCase				(   self				)    ->   Tuple:







        if not self.test_rust_tokenizer:
               return

        _A	  =    self.get_tokenizer()
        _A	  =    self.get_rust_tokenizer()

        _A	  =    """I was born in 92000, and this is falsé."""

        _A	  =    tokenizer.tokenize(lowercase_				)
        _A	  =    rust_tokenizer.tokenize(lowercase_				)
        self.assertListEqual(lowercase_						,							lowercase_				)

        _A	  =    tokenizer.encode(lowercase_						,							add_special_tokens=lowercase_				)
        _A	  =    rust_tokenizer.encode(lowercase_						,							add_special_tokens=lowercase_				)
        self.assertListEqual(lowercase_						,							lowercase_				)

        _A	  =    self.get_rust_tokenizer()
        _A	  =    tokenizer.encode(lowercase_				)
        _A	  =    rust_tokenizer.encode(lowercase_				)
        self.assertListEqual(lowercase_						,							lowercase_				)



 @slow
 def 	UpperCAmelCase				(   self				)    ->   int:
        _A	  =    """Hello World!"""
        _A	  =    [0, 3_53_78, 66_61, 38, 2]
        # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
        # xlmr.eval()
        # xlmr.encode(symbols)

        self.assertListEqual(lowercase_						,							self.big_tokenizer.encode(lowercase_				)				)



 @slow
 def 	UpperCAmelCase				(   self				)    ->   Tuple:
        _A	  =    (
            """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
            """ add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
        )
        _A	  =    [
            0,
            32_93,
            83,
            10,
            45_52,
            49_89,
            79_86,
            6_78,
            10,
            59_15,
            1_11,
            17_94_59,
            12_48_50,
            4,
            60_44,
            2_37,
            12,
            6,
            5,
            6,
            4,
            67_80,
            7_05,
            15,
            13_88,
            44,
            3_78,
            1_01_14,
            7_11,
            1_52,
            20,
            6,
            5,
            2_23_76,
            6_42,
            12_21,
            1_51_90,
            3_41_53,
            4_50,
            56_08,
            9_59,
            11_19,
            5_77_02,
            1_36,
            1_86,
            47,
            10_98,
            2_93_67,
            47,
            # 4426, # What fairseq tokenizes from "<unk>": "_<"
            # 3678, # What fairseq tokenizes from "<unk>": "unk"
            # 2740, # What fairseq tokenizes from "<unk>": ">"
            3,  # What we tokenize from "<unk>": "<unk>"
            6,  # Residue from the tokenization: an extra sentencepiece underline
            4,
            60_44,
            2_37,
            62_84,
            5_09_01,
            5_28,
            31,
            90,
            34,
            9_27,
            2,
        ]
        # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
        # xlmr.eval()
        # xlmr.encode(symbols)

        self.assertListEqual(lowercase_						,							self.big_tokenizer.encode(lowercase_				)				)



 @slow
 def 	UpperCAmelCase				(   self				)    ->   Any:
        _A	  =    {"""input_ids""": [[0, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 54_28, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 1_23_99, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 27_89, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 8_05, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [0, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 5_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
        # fmt: on

        self.tokenizer_integration_test_util(
            expected_encoding=lowercase_						,							model_name="""xlm-roberta-base"""						,							revision="""d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3"""						,							)





import numpy as np
import torch
import tqdm

from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler




class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''




 def __init__(		self				:   int							, lowercase_				:   UNetaDModel							, lowercase_				:   UNetaDModel							, lowercase_				:   DDPMScheduler							, lowercase_				:   Any							, )						->	Any:


   """simple docstring"""







   super().__init__()
   _UpperCamelCase           =    value_function
   _UpperCamelCase           =    unet
   _UpperCamelCase           =    scheduler
   _UpperCamelCase           =    env
   _UpperCamelCase           =    env.get_dataset()
   _UpperCamelCase           =    {}
   for key in self.data.keys():
     try:
       _UpperCamelCase           =    self.data[key].mean()
     except:  # noqa: E722
       pass
   _UpperCamelCase           =    {}
   for key in self.data.keys():
     try:
       _UpperCamelCase           =    self.data[key].std()
     except:  # noqa: E722
       pass
   _UpperCamelCase           =    env.observation_space.shape[0]
   _UpperCamelCase           =    env.action_space.shape[0]







 def 		__UpperCAmelCase				(		self				:   Any							, lowercase_				:   Optional[int]							, lowercase_				:   Optional[Any])						->	Tuple:


   """simple docstring"""







   return (x_in - self.means[key]) / self.stds[key]







 def 		__UpperCAmelCase				(		self				:   Any							, lowercase_				:   Tuple							, lowercase_				:   Optional[Any])						->	List[Any]:


   """simple docstring"""







   return x_in * self.stds[key] + self.means[key]







 def 		__UpperCAmelCase				(		self				:   List[Any]							, lowercase_				:   Optional[Any])						->	List[Any]:


   """simple docstring"""







   if type(lowercase_) is dict:
     return {k: self.to_torch(lowercase_) for k, v in x_in.items()}
   elif torch.is_tensor(lowercase_):
     return x_in.to(self.unet.device)
   return torch.tensor(lowercase_							, device=self.unet.device)







 def 		__UpperCAmelCase				(		self				:   Optional[Any]							, lowercase_				:   Any							, lowercase_				:   Union[str, Any]							, lowercase_				:   List[str])						->	Tuple:


   """simple docstring"""







   for key, val in cond.items():
     _UpperCamelCase           =    val.clone()
   return x_in







 def 		__UpperCAmelCase				(		self				:   Union[str, Any]							, lowercase_				:   Optional[int]							, lowercase_				:   str							, lowercase_				:   List[Any]							, lowercase_				:   int)						->	Dict:


   """simple docstring"""







   _UpperCamelCase           =    x.shape[0]
   _UpperCamelCase           =    None
   for i in tqdm.tqdm(self.scheduler.timesteps):
     # create batch of timesteps to pass into model
     _UpperCamelCase           =    torch.full((batch_size,)							, lowercase_							, device=self.unet.device							, dtype=torch.long)
     for _ in range(lowercase_):
       with torch.enable_grad():
         x.requires_grad_()

         # permute to match dimension for pre-trained models
         _UpperCamelCase           =    self.value_function(x.permute(0							, 2							, 1)							, lowercase_).sample
         _UpperCamelCase           =    torch.autograd.grad([y.sum()]							, [x])[0]

         _UpperCamelCase           =    self.scheduler._get_variance(lowercase_)
         _UpperCamelCase           =    torch.exp(0.5 * posterior_variance)
         _UpperCamelCase           =    model_std * grad

       _UpperCamelCase           =    0
       _UpperCamelCase           =    x.detach()
       _UpperCamelCase           =    x + scale * grad
       _UpperCamelCase           =    self.reset_xa(lowercase_							, lowercase_							, self.action_dim)

     _UpperCamelCase           =    self.unet(x.permute(0							, 2							, 1)							, lowercase_).sample.permute(0							, 2							, 1)

     # TODO: verify deprecation of this kwarg
     _UpperCamelCase           =    self.scheduler.step(lowercase_							, lowercase_							, lowercase_							, predict_epsilon=lowercase_)["prev_sample"]

     # apply conditions to the trajectory (set the initial state)
     _UpperCamelCase           =    self.reset_xa(lowercase_							, lowercase_							, self.action_dim)
     _UpperCamelCase           =    self.to_torch(lowercase_)
   return x, y







 def __call__(		self				:   Optional[int]							, lowercase_				:   str							, lowercase_				:   int=64							, lowercase_				:   Any=32							, lowercase_				:   List[Any]=2							, lowercase_				:   str=0.1)						->	Optional[int]:


   """simple docstring"""







   _UpperCamelCase           =    self.normalize(lowercase_							, "observations")
   _UpperCamelCase           =    obs[None].repeat(lowercase_							, axis=0)

   _UpperCamelCase           =    {0: self.to_torch(lowercase_)}
   _UpperCamelCase           =    (batch_size, planning_horizon, self.state_dim + self.action_dim)

   # generate initial noise and apply our conditions (to make the trajectories start at current state)
   _UpperCamelCase           =    randn_tensor(lowercase_							, device=self.unet.device)
   _UpperCamelCase           =    self.reset_xa(lowercase_							, lowercase_							, self.action_dim)
   _UpperCamelCase           =    self.to_torch(lowercase_)

   # run the diffusion process
   _UpperCamelCase      ,						_UpperCamelCase           =    self.run_diffusion(lowercase_							, lowercase_							, lowercase_							, lowercase_)

   # sort output trajectories by value
   _UpperCamelCase           =    y.argsort(0							, descending=lowercase_).squeeze()
   _UpperCamelCase           =    x[sorted_idx]
   _UpperCamelCase           =    sorted_values[:, :, : self.action_dim]
   _UpperCamelCase           =    actions.detach().cpu().numpy()
   _UpperCamelCase           =    self.de_normalize(lowercase_							, key="actions")

   # select the action with the highest value
   if y is not None:
     _UpperCamelCase           =    0
   else:
     # if we didn't run value guiding, select a random action
     _UpperCamelCase           =    np.random.randint(0							, lowercase_)

   _UpperCamelCase           =    denorm_actions[selected_index, 0]
   return denorm_actions




